This invention is directed to the video compression area which aims at reducing the bit-rate required to transmit and store a video content while maintaining at the same time an acceptable visual quality (lossy coding). Data compression in lossy video coding is achieved by discarding some redundant information in the source data. The lost information cannot be recovered at the decoder and this loss may introduce some quality degradation (artefacts). To limit the annoyance of artefacts, the information to be discarded should be carefully selected by efficiently exploiting the redundancy among video data. Lossy video coding techniques as those standardised by the H.264/AVC and H.265/HEVC standards aim at reducing data redundancy by exploiting both spatial and temporal correlation. In particular, spatial correlation allows prediction of the value of each sample by a set of neighbouring samples that lie outside the current block of samples. On the other hand, temporal correlation is exploited by predicting the value of one sample with the value of another sample which belongs to a different frame in temporal order. Both in spatial and temporal prediction, the predicted value is then subtracted from the current sample value and the difference is then transformed, quantised and entropy encoded using techniques know by those skilled in the art. Spatial transform provides an efficient representation of the prediction difference using a fewer number of coefficients. Quantisation instead performs the aforementioned data reduction by discarding or scaling the values of transform coefficients. Generally, the difference between an original sample and its prediction is called residual. Spatial transforms as the well-known Discrete Cosine Transform (DCT) are effective in image and video compression only if they can provide a representation of the input residuals which is sparse in the transform domain. In fact it is well known by those skilled in the art that entropy encoding techniques are very efficient over sparse signals. However, if the input residual is already sparse, the spatial transformation should not be applied as it may provide a number of coefficients different from zero which is higher than their counterparts in the input. When the spatial transform is skipped, the input residuals are then directly quantised and entropy encoded. It should noted that even when the transform is skipped because the input residuals are sparse, some spatial correlation may be still present and can be exploited by some sample-based prediction techniques. The signal obtained after residual prediction is called differential residual. Differential residuals can be quantised and entropy encoded in a similar way as residuals. Finally, it is also known by those skilled in the art that increasing the accuracy for the encoding processing (e.g. prediction, transformation, etc.) improves the compression efficiency of video codecs. However, this increased precision requires more storing resources to keep reconstructed data for future prediction.